Adaptive Control for Triadic Human-Robot-FES Collaboration in Gait Rehabilitation: A Pilot Study

TL;DR

An adaptive hybrid robot-FES controller is presented to enable the triadic collaboration between the patient, the robot and FES and suggest that the hybrid adaptive controller may be able to adapt to the behaviour of the user to provide assistance as needed and prevent the early termination of physical therapy due to muscle fatigue.

Abstract

The hybridisation of robot-assisted gait training and functional electrical stimulation (FES) can provide numerous physiological benefits to neurological patients. However, the design of an effective hybrid controller poses significant challenges. In this over-actuated system, it is extremely difficult to find the right balance between robotic assistance and FES that will provide personalised assistance, prevent muscle fatigue and encourage the patient's active participation in order to accelerate recovery. In this paper, we present an adaptive hybrid robot-FES controller to do this and enable the triadic collaboration between the patient, the robot and FES. A patient-driven controller is designed where the voluntary movement of the patient is prioritised and assistance is provided using FES and the robot in a hierarchical order depending on the patient's performance and their muscles' fitness. The performance of this hybrid adaptive controller is tested in simulation and on one healthy subject. Our results indicate an increase in tracking performance with lower overall assistance, and less muscle fatigue when the hybrid adaptive controller is used, compared to its non adaptive equivalent. This suggests that our hybrid adaptive controller may be able to adapt to the behaviour of the user to provide assistance as needed and prevent the early termination of physical therapy due to muscle fatigue.

Figures (6)

• Figure 1: A. Fatigue model identification for quadriceps and hamstrings using FES in isometric conditions (pushing a heavy box on an instrumented treadmill). B. Electrode placement. C. Experimental validation of hybrid adaptive controller.
• Figure 2: Illustration of the reference kinematic path in joint space, surrounded by the dead band, and the FES band. The magnified region depicts the components of the hybrid path controller for a human configuration, $q_{act}$.
• Figure 3: Simulated response of the hybrid controller. (a) Shows the robotic stiffness and assistance provided, (b) the trajectory error and adaptation of parameter $\gamma$, and (c) the muscle fatigue and stimulation provided with the adaptive controller during poor model performance (before grey dotted line) and more accurate performance (after grey dotted line).
• Figure 4: Comparison of the normalised trajectory error, robotic assistance, FES, and muscle fatigue between the exoskeleton controller (EPC), the FES controller (FPC), the hybrid controller (HPC) and the hybrid adaptive controller (HAPC).
• Figure 5: Experimental response of hybrid controller. Comparison between (a) the robotic stiffness and assistance, (b) trajectory error and (c) muscle fatigue and stimulation provided with the adaptive and non adaptive controllers for the left leg.